让实验室可计算，实现自主科研的可信流程
A computable representation of the physical laboratory enables verifiable workflows
- 用带类型的研究对象和能力约束操作构建实验室数字模型
- 生成可验证的科研流程，提前模拟并检查操作条件
- 适合自动化科研、智能实验系统研发者使用
使科学可计算需要对科学知识和科学验证所处的物理世界进行建模。本文通过类型化研究对象、能力约束操作和组合式工作流代数，建立可计算的物理实验室表示。它为机器可读知识提供物理世界对应物，将工作流表达为随实验状态演化的程序，明确包含依赖关系、决策、循环与并发。该表示在模块化代理型机器人实验室中实现，将形式化操作绑定到可执行的功能技能。针对不同科学目标生成相对能力的工作流，状态化仿真推进对象变换，并在执行前验证操作前提与实验室约束。所提表示及其工程框架共同构建了代理推理与能力约束物理变换之间的通用计算接口，为端到端自主科学发现奠定基础。
原文摘要 · Abstract (English)
Making science computable requires representations of both scientific knowledge and the physical world in which scientific claims are tested. A computable representation of the physical laboratory is established through typed research objects, capability-bound operations and a compositional workflow algebra. It provides the physical-world counterpart to machine-readable knowledge, expressing workflows as programs over evolving laboratory states with explicit dependencies, decisions, iteration and concurrency. The representation was implemented in a modular agentic robotic laboratory by binding formal operations to executable Function Skills. For diverse scientific intents, capability-relative workflows were generated, while stateful simulation propagated object transformations and verified operation preconditions and laboratory constraints before dispatch. The proposed representation and its engineering framework jointly establish a general computational interface between agent reasoning and capability-bound physical transformations, providing a foundation for end-to-end autonomous scientific discovery.
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